109 lines
3.1 KiB
Julia
109 lines
3.1 KiB
Julia
using SparseArrays
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using NuclearToolkit
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using SpecialFunctions
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include("helper.jl")
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# Gaussian potentials in HO space
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inv_factorial(n) = Iterators.prod(inv.(1:n))
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sqrt_factorial(n) = Iterators.prod(sqrt.(n:-1:1))
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N_lnk(l, n, k) = 1/sqrt_factorial(n) * binomial(n, k) * sqrt(gamma(n + l + 3/2)) / gamma(k + l + 3/2)
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Talmi(l, R, k1, k2; ω=1.0) = (-1)^(k1 + k2) * (1 + 1/(ω * R^2))^-(3/2 + l + k1 + k2) * gamma(3/2 + l + k1 + k2)
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V_Gaussian(R, l, n1, n2; ω=1.0) = (-1)^(n1 + n2) * better_sum([N_lnk(l, n1, k1) * N_lnk(l, n2, k2) * Talmi(l, R, k1, k2; ω=ω) for (k1, k2) in Iterators.product(0:n1, 0:n2)])
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function get_sp_basis(E_max)
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Es = Int[]
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ns = Int[]
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ls = Int[]
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# E = 2*n + l
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for E in 0 : E_max
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for n in 0 : E ÷ 2
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l = E - 2*n
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push!(Es, E)
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push!(ns, n)
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push!(ls, l)
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end
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end
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return (Es, ns, ls)
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end
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function get_2p_basis(E_max)
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Es = Int[]
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n1s = Int[]
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l1s = Int[]
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n2s = Int[]
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l2s = Int[]
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# E = 2*n1 + l1 + 2*n2 + l2
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for E in 0 : 2*E_max
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for n1 in 0 : E ÷ 2
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for n2 in 0 : (E - 2*n1) ÷ 2
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for l1 in 0 : (E - 2*n1 - 2*n2)
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l2 = E - 2*n1 - 2*n2 - l1
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push!(Es, E)
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push!(n1s, n1)
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push!(l1s, l1)
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push!(n2s, n2)
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push!(l2s, l2)
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end
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end
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end
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end
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return (Es, n1s, l1s, n2s, l2s)
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end
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function sp_T_matrix(ns, ls; mask=trues(length(ns),length(ns)), ω=1.0, μ=1.0)
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mat = spzeros(length(ns), length(ns))
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for idx in CartesianIndices(mat)
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if !mask[idx]; continue; end
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(i, j) = Tuple(idx)
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if ls[i] == ls[j]
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if ns[i] == ns[j]
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mat[idx] = ns[j] + ls[i]/2 + 3/4
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elseif abs(ns[i]-ns[j]) == 1
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n_max = max(ns[i], ns[j])
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mat[idx] = -(1/2) * sqrt(n_max * (n_max + ls[i] + 1/2))
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end
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end
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end
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return (ω / μ) .* mat
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end
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function sp_V_matrix(V_l, ns, ls; mask=trues(length(ns),length(ns)), dtype=Float64)
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mat = zeros(dtype, length(ns), length(ns))
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Threads.@threads for idx in CartesianIndices(mat)
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if !mask[idx]; continue; end
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(i, j) = Tuple(idx)
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if ls[i] == ls[j]
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mat[idx] = V_l(ls[i], ns[i], ns[j])
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end
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end
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return sparse(mat)
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end
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function Moshinsky_transform(Es, n1s, l1s, n2s, l2s, Λ)
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E_max = maximum(Es)
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j_max = 2 * E_max + 1
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l_max = j_max
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to = 0 # unused
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dtri = NuclearToolkit.prep_dtri(l_max + 1);
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dcgm0 = NuclearToolkit.prep_dcgm0(l_max);
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d6j = nothing # NuclearToolkit.prep_d6j_int(E_max, j_max, to);
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mat = spzeros(length(Es), length(Es))
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s = hcat(Es, n1s, l1s, n2s, l2s)
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for idx in CartesianIndices(mat)
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(i, j) = Tuple(idx)
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(Elhs, N, L, n, l) = s[i, :]
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(Erhs, n1, l1, n2, l2) = s[j, :]
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if Elhs == Erhs
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mat[i, j] = NuclearToolkit.HObracket_d6j(N, L, n, l, n1, l1, n2, l2, Λ, 1.0, dtri, dcgm0, d6j)
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end
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end
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return mat
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end
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